Handwritten Signature Research Articles

Toward Robust Classifiers for PDF Malware Detection

Malicious Portable Document Format (PDF) files represent one of the largest threats in the computer security space. Significant research has been done using handwritten signatures and machine learning based on detection via manual feature extraction. These approaches are time consuming, require substantial prior knowledge, and the list of features must be updated with each newly discovered vulnerability individually. In this study, we propose two models for PDF malware detection. The first model is a convolutional neural network (CNN) integrated into a standard deviation based regularization model to detect malicious PDF documents. The second model is a support vector machine (SVM) based ensemble model with three different kernels. The two models were trained and tested on two different datasets. The experimental results show that the accuracy of both models is approximately 100%, and the robustness against evasive samples is excellent. Further, the robustness of the models was evaluated with malicious PDF documents generated using Mimicus. Both models can distinguish the different vulnerabilities exploited in malicious files and achieve excellent performance in terms of generalization ability, accuracy, and robustness.

A Hybrid Technique based on RSA and Data Hiding for Securing Handwritten Signature

Data exchange has been significantly encouraged by the development of communication technology and the wide use of social media over the Internet. Therefore, it is important to hide the data transmitted, especially the data that requires a person’s signature. Where the signature is an increasingly needed item that is used in our daily life to achieve some paper-based authentication in departments, the individual himself needs the signature. Cryptography and steganography are commonly considered to be the most important data hiding methodologies. Steganography is used to hide the secret message in the carrier media, such as text, audio, video, and image files, without the carrier media being distorted, and cryptography is used to conceal the purpose of the secret message. A hybrid data hiding (image steganography) and encryption technique is implemented in this research on the time domain. The secret handwritten signature image is first encrypted using the public key algorithm (RSA) in the proposed technique, then randomly inserted using Embedding data process to be concealed in one of the last three bits of that pixel(1st Least Significant Bit, 2nd LSB, and 3rd LSB) based on mathematical randomized formula over all pixels of the carrier media (image). It is assumed that the process of randomization will increase the protection provided by the technique. The suggested technique is implemented on gray level cover images. As a consequence of the random scattering of bits and using encryption, it is noted that the proposed technique achieves enhanced data hiding results in terms of performance, protection, and imperceptibility properties and the histogram of the proposed technique is better and provides more protection and security than the ordinary sequential Least Significant Bit (LSB).

Biometryczne podpisy elektroniczne jako nowy przedmiot badań pismoznawczych

Analyses of electronic biometric signatures constitute an innovation in forensics. The aim of the study described in this article was to determine whether it is possible to categorically confirm or exclude both the authenticity and the execution of handwritten biometric electronic signatures. Several-year-long research on various types of electronic signatures has made it possible to formulate categorical conclusions in this area. The article defines and determines the terminological scope of the biometric electronic signature concept within the widely understood electronic signatures. The analyses of biometric signatures were based on the graphical-comparative method commonly used in the traditional model of andwriting analysis. The only modification consisted in replacing the set of motoric features with biometric features, which turned out to be necessary for a categorical opinion on this matter. Study results described in the text allow for quantitative examination within analysis of manuscripts thus enabling issuing a categorical opinion. The biometric features of handwriting identified entirely by means of digital data ought to contribute to the elimination of any bias that might exist on the part of an expert.

Self-Powered Intelligent Human-Machine Interaction for Handwriting Recognition.

Handwritten signatures widely exist in our daily lives. The main challenge of signal recognition on handwriting is in the development of approaches to obtain information effectively. External mechanical signals can be easily detected by triboelectric nanogenerators which can provide immediate opportunities for building new types of active sensors capable of recording handwritten signals. In this work, we report an intelligent human-machine interaction interface based on a triboelectric nanogenerator. Using the horizontal-vertical symmetrical electrode array, the handwritten triboelectric signal can be recorded without external energy supply. Combined with supervised machine learning methods, it can successfully recognize handwritten English letters, Chinese characters, and Arabic numerals. The principal component analysis algorithm preprocesses the triboelectric signal data to reduce the complexity of the neural network in the machine learning process. Further, it can realize the anticounterfeiting recognition of writing habits by controlling the samples input to the neural network. The results show that the intelligent human-computer interaction interface has broad application prospects in signature security and human-computer interaction.

Сучасний стан і перспективи розвитку банківських інновацій у контексті підвищення якості обслуговування клієнтів

In the context of globalization of the unstable situation in the financial market, the coronavirus pandemic, strengthened interbank competition, lack of financial resources and increasing their value, raising standards by the National Bank of Ukraine, it is imperative to improve the banking business management system by introducing innovations in the form of the use of new technologies that are adequate in view of development strategies and new banking products. That is why innovative development becomes the driving force of the efficiency of the banking sector’s activity. The article is aimed at evaluating the current state of innovative banking technologies and prospects for their development in Ukraine in the context of improving the quality of customer service. Priority directions of innovations for effective development of banking institutions and ways of their solution are defined. When analyzing trends in innovative activities of domestic banks, the major ones are identified. The most popular innovative products in the domestic banking market are listed and their characterizations are provided. A detailed analysis of normative and legislative acts is carried out, determining the prospects for the development of innovations in the banking sector in the near future and by 2025, such as: FinTech development strategy 2025; electronic payment system at the National Bank (EFT) in the 23/7 regime; service of sharing of digital documents; procedure for using a digital handwritten signature; creation and use of QR codes for credit transfers and when paying for goods and services; change of remote identification and verification of bank customers, etc.

Offline handwritten signature verification using various Machine Learning Algorithms

In today’s world it is necessary to protect one’s authenticity in order to ensure the protection of personal information that only the authenticate credentials of a person can have access to. Nowadays there is an increase in number of malpractices like signature forgery to access the important information of a person. To encounter signature verification problem, there have been a number of advances in verifying the authenticity of signature using various techniques including Machine Learning and Deep Learning. This paper introduces a novel approach to verify the signatures using difference of gaussian filtering technique, gray level co-occurrence matrix feature extraction technique, principle component analysis and kernel principal component analysis associated with various machine learning algorithms. The publicly available Kaggle offline handwritten signature dataset is used for training. This article compares the accuracy of the dataset on various machine learning algorithms. After training datasets the lowest accuracy achieved is 56.66% for Naive Bayes algorithm. The highest accuracy achieved is 82% for K-Nearest Neighbour (KNN) and 81.66% for Random Forest using principle components and kernel principle components of the dataset.

Wi-Fi Based User Identification Using In-Air Handwritten Signature

This paper conducts a feasibility study regarding the use of the Wi-Fi channel state information for user recognition based on in-air handwritten signatures. A novel system for identity recognition is thus proposed to observe for distinctive signal distortions along the propagation path for different users. The system capitalizes on the vast availability of Wi-Fi signals for signal analysis without needing additional hardware infra-structure. Since the patterns of the raw Wi-Fi signals are sensitive to the signer’s location, a transfer learning has been adopted to cope with the positional variation. Specifically, features trained at one position are transferred to classify signals collected at another position via a single shot retraining. A kernel and range space projection has been adopted for the single shot retraining. Our experiments show encouraging results for the proposed system.

Influence of destabilizing factors on the stability of user's handwritten signature indicators

Consideration is given to the question of user`s handwritten signature parameters informativeness and stability during authentication. An identifier that uses biometric characteristics is inextricably linked to the user and it is almost impossible to use it without authorization. It is proposed to use dynamic biometric characteristics of users. Their advantage is that due to the presence of a dynamic component, the probability of their forgery by an attacker is very low. A handwritten signature is used as a biometric characteristic of the user. A handwritten signature is a socially and legally recognized biometric characteristic used for human authentication. It has a rather complex structure and high detail - all this makes solving the problem of user identification by mathematical methods quite complex and requires high computational costs. Another significant disadvantage is that handwritten authentication systems require the installation of additional specialized equipment, which makes the use of such systems as an ordinary means of authentication very expensive. Nowadays the presence of mobile devices in almost all users has made it possible to form the idea of using them in authentication systems. Thanks to that a scheme for implementing a computer security system against unauthorized access based on handwritten signatures using Android-based mobile devices as signature input devices were proposed. An algorithm based on Heming's distance was chosen to implement user tolerance. According to the selected algorithm, a method for forming a biometric vector has been developed. The optimal characteristics are investigated and the efficiency of using the proposed form biometric characteristics vector is estimated. The speed of movement at certain intervals and the inclination angle of the vector interval were chosen as indicators of the handwritten signature. It is offered to estimate stability in time and dependence of the chosen biometric signs on the following factors: emotional and physical condition of the user, and also time of day at the moment of authentication. Developed a software application for the Android operating system, which collects the time characteristics and values of the proposed factors for time characteristics vectors, as well as allows to export the accumulated data.

SIGNATURE IDENTIFICATION AND VERIFICATION SYSTEMS: A COMPARATIVE STUDY ON THE ONLINE AND OFFLINE TECHNIQUES

Handwritten signature identification and verification has become an active area of research in recent years. Handwritten signature identification systems are used for identifying the user among all users enrolled in the system while handwritten signature verification systems are used for authenticating a user by comparing a specific signature with his signature that is stored in the system. This paper presents a review for commonly used methods for pre-processing, feature extraction and classification techniques in signature identification and verification systems, in addition to a comparison between the systems implemented in the literature for identification techniques and verification techniques in online and offline systems with taking into consideration the datasets used and results for each system

Сравнение применения локальных и глобальных признаков рукописной подписи для аутентификации пользователя

The paper compares two methods of user authentication based on a handwritten signature. The first is based on the extraction of local signature features as functions that depend on the time of its application. The second extracts global signature features that characterize it entirely without reference to the moments of application. Authentication algorithms are presented for each of the methods; experiments are carried out on real handwritten signatures obtained during the study.

CURRENT METHODS OF SIGNATURES’ TECHNICAL FORGING WITH THE USE OF TECHNICAL MEANS


 Taking into account a significant scientific and technological progress, use of modern monochrome or multicolor photocopiers in the office administration, and the increase in this regard in the number of signatures received for examination by forensic expert units, relevance for continuing to study signatures as one of the essential requisites of many types of documents and methods of their technical forgery becomes obvious.
 The purpose of the article is to highlight certain issues of current methods of signatures technical forgery.
 Research methods: general (formal-logical methods of cognition: analysis, synthesis, generalization, analogy), general scientific (observation, measurement, description, comparison), special (visual, microscopic, photographic).
 Since the fact of handwritten signature execution possesses legal value, it often becomes the object of forgery. For this purpose, a variety of techniques is used. Criminals often resort to the so-called technical forgery of signatures on documents (performing signatures on behalf of another person using various techniques and technical means which helps to achieve the greatest similarity with the original) along with the usual graphic forgery. The essence of signatures technical forgery usually lies in mechanical copying, instead of imitating features of writing-motor skills and the signature-original. By such a signature, you can not measure the level of writing-motor skills of the performer.
 Each of the methods of signature technical forgery leaves in the signature the signs inherent only to this method. Studying such methods and their features collectively will allow to diagnose the fact of handwritten or technical execution of a signature with a sufficiently high degree of probability.

Offline signatures matching using haar wavelet subbands

The complexity of multimedia contents is significantly increasing in the current world. This leads to an exigent demand for developing highly effective systems tosatisfy human needs. Until today, handwritten signature considered an important means that is used in banks and businesses to evidence identity, so there are many works triedto develop a method for recognition purpose. This paper introduced an efficient technique for offline signature recognition depending on extracting the local feature by utilizing the haar wavelet subbands and energy. Three different setsof features are utilized by partitioning the signature image into non overlapping blocks where different block sizes are used. CEDAR signature database is used asa dataset for testing purpose. The results achieved by this technique indicate a high performance in signature recognition.

Bayesian multivariate models for case assessment in dynamic signature cases

Dynamic signatures are recordings of signatures made on digitizing devices such as tablet PCs. These handwritten signatures contain both dynamic and spatial information on every data point collected during the signature movement and can therefore be described in the form of multivariate data. The management of dynamic signatures represents a challenge for the forensic science community through its novelty and the volume of data available. Much as for static signatures, the authenticity of dynamic signatures may be doubted, which leads to a forensic examination of the unknown source signature.The Bayes’ factor, as measure of evidential support, can be assigned with statistical models to discriminate between competing propositions. In this respect, the limitations of existing probabilistic solutions to deal with dynamic signature evidence is pointed out and explained in detail. In particular, the necessity to remove the independence assumption between questioned and reference material is emphasized.

A Secured Digital Handwritten Signature Prototype for Visually Impaired People

A Secured Digital Handwritten Signature Prototype for Visually Impaired People

Black-box attack against handwritten signature verification with region-restricted adversarial perturbations

Handwritten signature verification is used to verify the identity of individuals through recognizing their signatures. Adversarial examples can induce misclassification, hence posing a severe threat to signature verification. At present, a variety of adversarial example attacks have been developed for image classification, but they are not that useful for attacking signature verification due to two main reasons. First, adversarial perturbations are likely to be imposed on the background of signature images, making them perceptible to human eyes. Second, perfect knowledge about signature verification systems is actually unavailable to attackers. Therefore, how to generate effective and stealthy signature adversarial examples is still an open issue. To shed insights on this challenging problem, we propose the first black-box adversarial example attack against handwritten signature verification in this paper. Our method has two key designs. First, its perturbations are intentionally restricted to the foreground (i.e., strokes) of signature images, which reduces the risk of being recognized by humans. Second, a gradient-free method is developed to achieve the desired perturbations through iteratively updating their positions and optimizing their intensity. Extensive experiments confirm the three advantages of our method. First, the adversarial perturbations generated by our method are almost invisible, while those generated by existing methods are more well-marked. Second, our method defeats the state-of-the-art signature verification method with a surprisingly high success rate of 92.1%. Last, our method breaks through the defense of background cleaning, although this defense can deactivate almost all the existing adversarial example attacks towards signature verification.

An Ensemble Based Offline Handwritten Signature Verification System

In the field of security and forgery prevention, handwritten signatures are the most widely recognized biometric since long and also most practical. Although handwritten signature verification systems are studied using both On-line and Off-line approaches, Off-line signature verification systems are more difficult to compare to On-line verification systems. This is due to the lack of dynamic information, viz. a database which constantly stores the latest signature of the person. In the paper an approach using ensemble methods are adopted to classify a signature as forgery or not. In proposed system, three classifiers, viz, one unsupervised, viz. Fuzzy C-Means (FCM) and two supervised classifiers, viz. Naive Bayes (NB) and Support Vector Machine (SVM) are used as base classifiers. An attempt is made to compare the different approaches. We attempt both the categories of classification not only because both of them are applicable in this particular case but also with an objective of finding out which performs better. In most cases it is observed that Naive Bayes and Ensemble are comparable as they exhibit better performance than the other two. But among them, in most of the cases Ensemble classifier performs better than the Naive Bayes and consequently we have taken the Ensemble as a final classifier.

A novel method for digital data encoding-decoding

Cryptography is one of the paramount and most vital data treatment processes, it allows us to be secure in our electronic transactions. The process of cryptography protects our valuable data such as private account numbers and transaction amounts, electronic signatures replace handwritten signatures or credit card authorizations, and public-key encryption provides confidentiality. The objective of data encryption is to keep digital data confidentiality save as it is stored on computer systems and transferred using the internet or other computer networks. In this paper we will focus in enhancing security level of the encryption-decryption process by introducing a novel method, which uses any digital color image to encode-decode secret message, the using of a special key to encrypt-decrypt the encoded-decoded message, the color image will be known only by the transmitter and receiver to keep the process of data treatment confidential, the obtained experimental results by the proposed method will be analyzed to prove the enhancement in process efficiency and confidentiality.

Deep learning-based data augmentation method and signature verification system for offline handwritten signature

Offline handwritten signature verification is a challenging pattern recognition task. One of the most significant limitations of the handwritten signature verification problem is inadequate data for training phases. Due to this limitation, deep learning methods that have obtained the state-of-the-art results in many areas achieve quite unsuccessful results when applied to signature verification. In this study, a new use of Cycle-GAN is proposed as a data augmentation method to address the inadequate data problem on signature verification. We also propose a novel signature verification system based on Caps-Net. The proposed data augmentation method is tested on four different convolutional neural network (CNN) methods, VGG16, VGG19, ResNet50, and DenseNet121, which are widely used in the literature. The method has provided a significant contribution to all mentioned CNN methods’ success. The proposed data augmentation method has the best effect on the DenseNet121. We also tested our data augmentation method with the proposed signature verification system on two widely used databases: GPDS and MCYT. Compared to other studies, our verification system achieved the state-of-the-art results on MCYT database, while it reached the second-best verification result on GPDS.

An investigation of the predictability of the Brazilian three-modal hand-based behavioural biometric: a feature selection and feature-fusion approach

New security systems, methods or techniques need to have their performance evaluated in conditions that closely resemble a real-life situation. The effectiveness with which individual identity can be predicted in different scenarios can benefit from seeking a broad base of identity evidence. Many approaches to the implementation of biometric-based identification systems are possible, and different configurations are likely to generate significantly different operational characteristics. The choice of implementational structure is, therefore, very dependent on the performance criteria, which is most important in any particular task scenario. The issue of improving performance can be addressed in many ways, but system configurations based on integrating different information sources are widely adopted in order to achieve this. Thus, understanding how each data information can influence performance is very important. The use of similar modalities may imply that we can use the same features. However, there is no indication that very similar (such as keyboard and touch keystroke dynamics, for example) basic biometrics will perform well using the same set of features. In this paper, we will evaluate the merits of using a three-modal hand-based biometric database for user prediction focusing on feature selection as the main investigation point. To the best of our knowledge, this is the first thought-out analysis of a database with three modalities that were collected from the same users, containing keyboard keystroke, touch keystroke and handwritten signature. First, we will investigate how the keystroke modalities perform, and then, we will add the signature in order to understand if there is any improvement in the results. We have used a wide range of techniques for feature selection that includes filters and wrappers (genetic algorithms), and we have validated our findings using a clustering technique.

Multilanguage-handwriting self-powered recognition based on triboelectric nanogenerator enabled machine learning

Handwriting signature is widely used and the main challenge for handwriting recognition is how to obtain comprehensive handwriting information. Triboelectric nanogenerator is sensitive to external triggering force and can be used to record personal handwriting signals and associated characteristics. In this work, micro/nano structure textured TENG acting as a smart self-powered handwriting pad is developed and its effectiveness for handwriting recognition is demonstrated. Three individuals' handwriting signals of English words, Chinese characters and Arabic numerals are acquired by leaf-inspired TENG, and the other three people's handwriting signals of English sentences and the corresponding Chinese sentences are obtained by cylindrical microstructured PDMS based TENG, and these signals exhibit unique features. Combined with the machine learning method, the people's handwriting was successfully identified. The classification accuracies of 99.66%, 93.63%, 91.36%, 99.05%, and 97.73% were reached for English words, Arabic numerals, Chinese characters, English sentences, and the corresponding Chinese sentences, respectively. The results strongly suggested that the textured TENG exhibited great potential in personal handwriting signature identification, security defense, and private information protection applications. This work presented a killer approach for multilanguage handwriting signal sensing and recording. We reported a smart self-powered handwriting pad based on micro/nano-structures textured TENG for sensing different individuals’ handwriting. The obtained signals exhibited unique features both in time domain and frequency domain which could be used in personal handwriting signature identification. For the first time, a novel solution for recognizing personal handwriting combing TENG based smart self-powered device, advanced signal processing and machine learning method was reported. We successfully demonstrated that extremely high classification accuracies achieved by using the proposed advanced technology. • TENG based self-powered handwriting pad was designed and fabricated. • The handwriting pad was sensitive and rapidly-responding to handwriting behavior. • Handwriting was recognized by combing the handwriting pad and machine learning method. • Extremely high classification accuracies were obtained for three kinds of characters.